Asha Sharma: recommendation

Transcript context

…Let me zoom out a little bit and ask you this question. You work with a bunch of companies that are building AI products on your platform, other platforms. I imagine some just do an awesome job and are killing it, some are struggling. What do you find are common patterns across the companies that do really well and have a lot of success building really successful AI products and ones that don't? Yeah, so I think there's things that are more broadly applying to the organization themselves and then there's things that are applying to the people who are building the AI products too. So more broadly, I think there's a pattern that's starting to emerge for successful companies. One is they are embracing AI and everybody becomes AI fluent. So I think everybody is using some sort of co-pilot or sort of AI in their day-to-day workflows like job one, so everyone's not afraid of it, understands how we can raise the ceiling and lower the floor for all sorts of skills and tasks. Number two, from there, they start to say, "Okay, how can I take a process that already exists and apply AI to making it better?" That might be something like customer support or taking fraud down from 15 days to cure to 10 days. In going through that entire loop of mapping out the process, applying AI to it, seeing some sort of impact, and then feeling the P&L or the intrinsic benefits that looks like. The third thing then is like, "Okay, great. Now that you've seen impact, everybody is using it, how do you actually use it to inflect growth?" And that can be something like improving the customer experience, so your LTV or retention improves. It could be co-creating a new set of concepts or categories. It could be going from agents that are embedded to agents that are embodied and then being able to take on exponential number of tasks. I think that where companies fail is that they're doing AI for AI's sake. They have a ton of projects that they're kicking off at the same time without a blueprint to understand how it actually worked and what their Stack looks like and they aren't treating it like a real investment, and so they don't have the measurement and the observability and the evals all set up. It's going to do that end to end. I think the tricky thing is for enterprises is the technology is changing. There's something like 70,000 enterprise tools in the AI space launched last year. It's really hard to know which one you should use for what outcome. And so you really need to bet on a platform or some app server type layer that allows you to swap things in and out and not really be beholden to anything, any one technology or any one tool because the reality is the whole thing is going to change. I feel like you have to actually build for the slope instead of the snapshot of where you are. So that's kind of what I see at the enterprise level. I think the builders themselves are actually changing pretty fundamentally too. Every single advent change a technology has invented a changing set of roles like mainframes to PCs like the whole garage engineers, and then when we went from server to cloud and mobile, there was like SEO specialists and CDNs and growth VMs and UXR and front end, back end, and yada yada. es to PCs like the whole garage engineers, and then when we went from server to cloud and mobile, there was like SEO specialists and CDNs and growth VMs and UXR and front end, back end, and yada yada. And now I think we're seeing this advent of the polymath and where I think that full stack builders are kind of having their renaissance where if you take an average organization, it takes probably 10 steps to launch a product. It could be security review, it could be spec, it could be user research, and there's what? Five plus functions, maybe six or seven. I'm being generous for a normal organization, and then you have six or seven layers. So all of a sudden, you have 500 different touch points that have to happen to get a product out and when there are 500 models available a week or 500 new technologies, that is insufficient. And so I really believe in the concept of the full stack builder. You're seeing it with a bunch of the AI native companies that are coming up. I'm even seeing it in enterprises that have been around for 50 years starting to operate in that way. And I think that gives you velocity and throughput and then gives you the whole loop to start to actually metabolize and go through that much faster.…

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